Balancing population convergence and diversity remains a significant challenge in constrained multi-objective evolutionary algorithms. To remedy this issue, this article proposes a constrained multi-objective evolutionary algorithm based on average vector angle and dynamic reduction mechanism (CMOEA-BAD). This algorithm designs a main population and an auxiliary population, which evolve independently. The main population is dedicated to solving the original problem, while the auxiliary problem focuses on solving the ancillary questions. On the one hand, the CMOEA-BAD takes into account the angle information of the ideal and lowest points of the main population, designs an average vector angle, and selects individuals based on this vector angle through constraint dominance principles to achieve the goal of balancing population diversity and convergence. On the other hand, this article proposes a population size dynamic reduction mechanism for the auxiliary population, which dynamically adjusts the size of the auxiliary population to reduce the computational resources it occupies during the evolution process, in order to accelerate the convergence speed of the algorithm. In order to verify the performance of the algorithm, the proposed algorithm is compared with PPS, BiCo, NSBiDiCo, MFOSPEA2, and CMOES algorithms in MW and DTLZ test problems, and applied to practical engineering problems. Experimental results show that the proposed algorithm can not only effectively balance the convergence and diversity of population, but also significantly improve the convergence speed of the algorithm. The overall running time of the algorithm has been shortened by 28%, and the overall performance is better.